CASE STUDY - 2 :: Apple Foliar Disease Detection 🍎🌳🍏

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PROJECT Documents 💼

Business Deck

DATASETS Used 🍃

NOTEBOOK Description 📗

This notebook contains the below components:

NOTEBOOK Contents ✍️

1. Libraries Import

2. Dataset Import

Models Set - 1

Import_Libraries

Dataset_Import

Data_Preparation

ORIGINAL DATASET :: Images names & their tgt labels

PRE AUGMENTATION :: Distribution of TGT Classes in TRAIN & TEST sets

TRAIN

OBSERVATION

TEST

OBSERVATION

Reading the images
Image Data Generator

Image_Augmentation

POST AUGMENTATION :: Distribution of TGT Classes in TRAIN & VALIDATION sets

TRAIN

OBSERVATION

VALIDATION

OBSERVATION

Calculating_Class_Weights

Defining_Performance_Metrics

Models_Training_Configuration

Models_Set_1

A.Global_Tuning---Custom_TopLayers---ImageNet_Weights

A1.ResNet---50

Evaluation on VALIDATION Set

OBSERVATIONS

Evaluation on TEST Set

OBSERVATIONS

Running TensorBoard - A1

model_4

OBSERVATIONS

A2.DenseNet---121

Evaluation on VALIDATION Set

OBSERVATIONS

Evaluation on TEST Set

OBSERVATIONS

Running TensorBoard - A2

model_5

OBSERVATIONS

A3.MobileNet---V1

Evaluation on VALIDATION Set

OBSERVATIONS

Evaluation on TEST Set

OBSERVATIONS

Running TensorBoard - A3

model_6

OBSERVATIONS

A4.MobileNet---V3---Small

Evaluation on VALIDATION Set

OBSERVATIONS

Evaluation on TEST Set

OBSERVATIONS

Running TensorBoard - A4

model_7

OBSERVATIONS

A5.EfficientNet---V2---B0

Evaluation on VALIDATION Set

OBSERVATIONS

Evaluation on TEST Set

OBSERVATIONS

Running TensorBoard - A5

model_8

OBSERVATIONS

OVERALL_RESULTS

models1_res_new

SUMMARY